Llama-2-7b
Maintainer: meta-llama
3.9K
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| Property | Value |
|---|---|
| Model Link | View on HuggingFace |
| API Spec | View on HuggingFace |
| Github Link | No Github link provided |
| Paper Link | No paper link provided |
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Model Overview
Llama-2-7b is a 7 billion parameter pretrained and fine-tuned generative text model developed by Meta. It is part of the Llama 2 family of large language models (LLMs) that range in size from 7 billion to 70 billion parameters. The fine-tuned Llama-2-Chat models are optimized for dialogue use cases and outperform open-source chat models on most benchmarks, performing on par with closed-source models like ChatGPT and PaLM in human evaluations for helpfulness and safety.
Model Inputs and Outputs
Inputs
- Text: The model takes text as input.
Outputs
- Text: The model generates text as output.
Capabilities
The Llama-2-7b model demonstrates strong performance across a range of academic benchmarks, including commonsense reasoning, world knowledge, reading comprehension, and math. It also shows improved safety characteristics compared to previous models, with higher truthfulness and lower toxicity on evaluation datasets.
What Can I Use It For?
Llama-2-7b is intended for commercial and research use in English. The fine-tuned Llama-2-Chat models can be used for assistant-like dialogue, while the pretrained models can be adapted for a variety of natural language generation tasks. Developers should follow the specific formatting guidelines provided by Meta to get the expected features and performance for the chat versions.
Things To Try
When using Llama-2-7b, it's important to keep in mind that as with all large language models, the potential outputs cannot be fully predicted in advance. Developers should perform thorough safety testing and tuning tailored to their specific applications before deployment. See the Responsible Use Guide for more information on the ethical considerations and limitations of this technology.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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